Hypothesis Testing Guide
Hypothesis testing is a formal process for utilizing statistics to investigate our views about the world. Scientists usually use it to test specific predictions that come from theories. Hypotheses are the names for these ideas.
In hypothesis testing, there are five key steps:
- Your study hypothesis should be stated as a null hypothesis (Ho) and an alternate hypothesis (Ho) (Ha or H1).
- Collect data in a method that will allow you to test your theory.
- Perform a statistical test that is appropriate.
- Decide if your null hypothesis should be rejected or not.
- In your results and discussion section, present your findings.
The technique you’ll use to test a hypothesis will always follow some form of these stages, but the specifics may vary.
Step 1: Identify your null and alternative hypotheses.
It’s crucial to restate your initial research hypothesis (the prediction you wish to examine) as a null (Ho) and alternate (Ha) hypothesis so that you can test it quantitatively.
Your first hypothesis, which predicts a relationship between variables, is usually the alternate hypothesis. The null hypothesis says that the variables you’re interested in have nothing to do with each other.
Step 2: Gather Information
For a statistical test to be valid, you must sample and collect data in a way that is designed to test your hypothesis. If your data aren’t representative of the population you’re interested in, you can’t use statistics to learn anything about it.
Step 3: Conduct a statistical analysis.
There are a variety of statistical tests available, but they all compare within-group variance (how spread out the data is inside a category) against between-group variance (how spread out the data is between categories) (how different the categories are from one another).
Your statistical test will produce a low p-value if the between-group variance is large enough that there is little or no overlap between groups. This suggests that the disparities between these groups are unlikely to have occurred by chance.
If the within-group variance is great but the between-group variance is low, your statistical test will reflect this with a high p-value. This means that any difference you find across groups is most likely attributable to chance.
The type of data you collected will determine which statistical test you use.
Step 4: Determine whether your null hypothesis should be rejected or not.
You must determine whether to reject or not to reject your null hypothesis based on the results of your statistical test.
In most circumstances, you’ll base your judgment on the p-value provided by the statistical test. For others, your predetermined level of significance for rejecting the null hypothesis will be 0.05, which means there’s a less than 5% chance you’d observe these results if the null hypothesis were true.
At certain instances, researchers adopt a lower level of significance, such as 0.01 in some cases (1 percent ). This reduces the chances of rejecting the null hypothesis mistakenly (Type I error).
Step 5: Make a presentation of your findings.
The outcomes of hypothesis testing will be provided in your research paper’s results and discussion sections.
In the findings section, you should include a quick overview of the data as well as a summary of the statistical test results (for example, the estimated difference between group means and associated p-value).
We talk about rejecting or failing to reject the null hypothesis in the technical language of hypothesis testing.
When presenting study findings in academic articles, however, we rarely speak in this manner. Instead, we return to our alternate hypothesis (in this case, the hypothesis that males are on average taller than women) and determine if our test result was consistent or inconsistent with it.
These are minor distinctions; it is clear that they both signify the same thing.
You’ll notice that we don’t indicate whether we accept or reject the alternative hypothesis. Because hypothesis testing does not prove or deny anything, this is the case. Its sole purpose is to see if a pattern we’re looking at might have happened by accident.
We can state our test supports our hypothesis if we reject the null hypothesis based on our research (i.e., we think it implausible that the pattern arose by coincidence). However, if the pattern fails to pass our decision criteria, indicating that it could have evolved by chance, we call the test inconclusive.
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